Análisis de modelos SOM - Frecuencia datos de entrada: semana

Modelo

• ID: 154
• Descripción:
• Frecuencia: semana
• Variables: fecha_cnt, precip, longitud, latitud, altitud
• Dimensiones del mapa: 5,5
• Iteraciones: 10000
• Parámetros adicionales:

source("../../lib/som-utils.R")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

source("../../lib/maps-utils.R")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Carga del modelo desde disco

mpr.set_base_path_analysis()
model <- mpr.load_model("som-154.rds.xz")
summary(model)

SOM of size 5x5 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 156652 objects.
Mean distance to the closest unit in the map: 0.818.

plot(model, type="changes")

Carga del dataset de entrada

df <- mpr.load_data("datos_semana_2k.csv.xz")

df

summary(df)

 id_estacion           fecha             fecha_cnt          tmax       
 Length:156652      Length:156652      Min.   : 1.00   Min.   :-109.0  
 Class :character   Class :character   1st Qu.:13.00   1st Qu.: 147.0  
 Mode  :character   Mode  :character   Median :27.00   Median : 201.0  
                                       Mean   :26.53   Mean   : 202.1  
                                       3rd Qu.:40.00   3rd Qu.: 262.0  
                                       Max.   :53.00   Max.   : 442.0  
      tmin             precip           nevada    prof_nieve      
 Min.   :-189.00   Min.   :  0.00   Min.   :0   Min.   :   0.000  
 1st Qu.:  48.00   1st Qu.:  0.00   1st Qu.:0   1st Qu.:   0.000  
 Median :  98.00   Median :  3.00   Median :0   Median :   0.000  
 Mean   :  98.13   Mean   : 16.92   Mean   :0   Mean   :   0.604  
 3rd Qu.: 152.00   3rd Qu.: 20.00   3rd Qu.:0   3rd Qu.:   0.000  
 Max.   : 272.00   Max.   :690.00   Max.   :0   Max.   :1073.000  
    longitud        latitud           altitud      
 Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:39.01   1st Qu.: -4.850   1st Qu.:  44.0  
 Median :41.22   Median : -1.411   Median : 263.0  
 Mean   :40.05   Mean   : -2.426   Mean   : 478.5  
 3rd Qu.:42.19   3rd Qu.:  1.272   3rd Qu.: 687.0  
 Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Carga de los mapas

world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")

Mapa de densidad

plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)

Número de elementos en cada celda:

nb <- table(model$unit.classif)
print(nb)

    1     2     3     4     5     6     7     8     9    10    11    12    13 
11470  5939  8567  4263  3651 15037  4082  1697 10272  5205  6481  5617  2869 
   14    15    16    17    18    19    20    21    22    23    24    25 
 5990 10183  5900  6505  5168  5958  2881 11868  5240  9753  1699   357 

Comprobación de nodos vacíos:

dim_model <- 5*5;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
  print(paste("[Warning] Existen nodos vacíos: ", len_nb, "/", dim_model))
}

Mapa de distancia entre vecinos

plot(model, type="dist.neighbours", shape = "straight")

Influencia de las variables

model_colnames = c("fecha_cnt", "precip", "longitud", "latitud", "altitud")
model_ncol = length(model_colnames)

Mapa de variables.

plot(model, shape = "straight")

Mapa de calor por variable

par(mfrow=c(3,4))
for (j in 1:model_ncol) {
  plot(model, type="property", property=getCodes(model,1)[,j],
    palette.name=mpr.coolBlueHotRed,
    main=model_colnames[j],
    cex=0.5, shape = "straight")
}

Correlación para cada columna del vector de nodos

if (!empty_nodes) {
  cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
  print(cor)
}

      fecha_cnt     precip   longitud    latitud    altitud
[1,] -0.2395832 0.13790137 -0.1086807 -0.3415295  0.5538646
[2,] -0.5401854 0.08290181 -0.5233902 -0.4396042 -0.3932068

Representación de cada variable en un mapa de factores:

if (!empty_nodes) {
  par(mfrow=c(1,1))
  plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
  lines(c(-1,1),c(0,0))
  lines(c(0,0),c(-1,1))
  text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
  symbols(0,0,circles=1,inches=F,add=T)
}

Importancia de cada variable - varianza ponderada por el tamaño de la celda:

if (!empty_nodes) {
  sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
     {m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
     effectif=nb))
  print(sort(sigma2,decreasing=T))
}

 longitud   latitud    precip fecha_cnt   altitud 
0.9658578 0.9329411 0.9038007 0.8876858 0.8861468 

Clustering

if (!empty_nodes) {
  hac <- mpr.hac(model, nb)
}

Visualización de 3 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=3)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=3)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip      
 Min.   : 1.00   Min.   :-109.0   Min.   :-189.00   Min.   :  0.00  
 1st Qu.:13.00   1st Qu.: 144.0   1st Qu.:  45.00   1st Qu.:  0.00  
 Median :27.00   Median : 196.0   Median :  93.00   Median :  4.00  
 Mean   :26.53   Mean   : 200.4   Mean   :  93.72   Mean   : 17.01  
 3rd Qu.:40.00   3rd Qu.: 262.0   3rd Qu.: 145.00   3rd Qu.: 22.00  
 Max.   :53.00   Max.   : 442.0   Max.   : 272.00   Max.   :236.00  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:39.88   1st Qu.: -4.049  
 Median :0   Median :   0.0000   Median :41.38   Median : -1.033  
 Mean   :0   Mean   :   0.6324   Mean   :40.83   Mean   : -1.521  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.24   3rd Qu.:  1.363  
 Max.   :0   Max.   :1073.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  58.0  
 Median : 316.0  
 Mean   : 485.2  
 3rd Qu.: 704.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

Visualización de 4 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=4)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=4)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   : 1.00   Min.   :-31.0   Min.   :-189.00   Min.   :  0.00   Min.   :0  
 1st Qu.:13.00   1st Qu.:152.0   1st Qu.:  52.00   1st Qu.:  0.00   1st Qu.:0  
 Median :26.00   Median :203.0   Median :  98.00   Median :  3.00   Median :0  
 Mean   :26.52   Mean   :208.5   Mean   :  99.28   Mean   : 15.97   Mean   :0  
 3rd Qu.:40.00   3rd Qu.:267.0   3rd Qu.: 148.00   3rd Qu.: 20.00   3rd Qu.:0  
 Max.   :53.00   Max.   :442.0   Max.   : 272.00   Max.   :236.00   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   :  0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.0000   1st Qu.:39.56   1st Qu.: -4.488   1st Qu.:  52.0  
 Median :  0.0000   Median :41.29   Median : -1.293   Median : 258.0  
 Mean   :  0.0771   Mean   :40.73   Mean   : -1.677   Mean   : 367.7  
 3rd Qu.:  0.0000   3rd Qu.:42.12   3rd Qu.:  1.296   3rd Qu.: 639.0  
 Max.   :599.0000   Max.   :43.57   Max.   :  4.216   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin             precip      
 Min.   : 1.00   Min.   :-109.0   Min.   :-175.00   Min.   :  0.00  
 1st Qu.:14.00   1st Qu.:  30.0   1st Qu.: -32.00   1st Qu.:  3.00  
 Median :27.00   Median :  78.0   Median :  11.00   Median : 18.00  
 Mean   :26.81   Mean   :  84.4   Mean   :  14.45   Mean   : 31.78  
 3rd Qu.:40.00   3rd Qu.: 138.0   3rd Qu.:  63.00   3rd Qu.: 47.00  
 Max.   :53.00   Max.   : 299.0   Max.   : 183.00   Max.   :225.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.4992  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:42.18   1st Qu.:  0.7317  
 Median :0   Median :   0.000   Median :42.47   Median :  1.0544  
 Mean   :0   Mean   :   8.542   Mean   :42.23   Mean   :  0.7103  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.64   3rd Qu.:  1.7150  
 Max.   :0   Max.   :1073.000   Max.   :42.77   Max.   :  2.4378  
    altitud    
 Min.   :1097  
 1st Qu.:1971  
 Median :2230  
 Mean   :2159  
 3rd Qu.:2400  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

Visualización de 5 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=5)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=5)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   : 1.00   Min.   :-31.0   Min.   :-189.00   Min.   :  0.00   Min.   :0  
 1st Qu.:14.00   1st Qu.:154.0   1st Qu.:  51.00   1st Qu.:  0.00   1st Qu.:0  
 Median :27.00   Median :206.0   Median :  99.00   Median :  3.00   Median :0  
 Mean   :26.73   Mean   :210.3   Mean   :  99.84   Mean   : 12.44   Mean   :0  
 3rd Qu.:40.00   3rd Qu.:269.0   3rd Qu.: 150.00   3rd Qu.: 17.00   3rd Qu.:0  
 Max.   :53.00   Max.   :442.0   Max.   : 272.00   Max.   :131.00   Max.   :0  
   prof_nieve           longitud        latitud          altitud      
 Min.   :  0.00000   Min.   :35.28   Min.   :-8.624   Min.   :   1.0  
 1st Qu.:  0.00000   1st Qu.:39.49   1st Qu.:-4.127   1st Qu.:  52.0  
 Median :  0.00000   Median :41.22   Median :-1.229   Median : 261.0  
 Mean   :  0.05786   Mean   :40.71   Mean   :-1.633   Mean   : 370.8  
 3rd Qu.:  0.00000   3rd Qu.:42.08   3rd Qu.: 1.296   3rd Qu.: 639.0  
 Max.   :299.00000   Max.   :43.57   Max.   : 4.216   Max.   :1405.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin             precip      
 Min.   : 1.00   Min.   :-109.0   Min.   :-175.00   Min.   :  0.00  
 1st Qu.:14.00   1st Qu.:  30.0   1st Qu.: -32.00   1st Qu.:  3.00  
 Median :27.00   Median :  78.0   Median :  11.00   Median : 18.00  
 Mean   :26.81   Mean   :  84.4   Mean   :  14.45   Mean   : 31.78  
 3rd Qu.:40.00   3rd Qu.: 138.0   3rd Qu.:  63.00   3rd Qu.: 47.00  
 Max.   :53.00   Max.   : 299.0   Max.   : 183.00   Max.   :225.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.4992  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:42.18   1st Qu.:  0.7317  
 Median :0   Median :   0.000   Median :42.47   Median :  1.0544  
 Mean   :0   Mean   :   8.542   Mean   :42.23   Mean   :  0.7103  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.64   3rd Qu.:  1.7150  
 Max.   :0   Max.   :1073.000   Max.   :42.77   Max.   :  2.4378  
    altitud    
 Min.   :1097  
 1st Qu.:1971  
 Median :2230  
 Mean   :2159  
 3rd Qu.:2400  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip          nevada 
 Min.   : 1.00   Min.   :-13.0   Min.   :-44.0   Min.   : 55.0   Min.   :0  
 1st Qu.: 8.00   1st Qu.:126.0   1st Qu.: 56.0   1st Qu.: 85.0   1st Qu.:0  
 Median :15.00   Median :154.0   Median : 81.0   Median :112.0   Median :0  
 Mean   :20.26   Mean   :158.3   Mean   : 83.1   Mean   :118.2   Mean   :0  
 3rd Qu.:36.00   3rd Qu.:187.0   3rd Qu.:111.0   3rd Qu.:144.0   3rd Qu.:0  
 Max.   :53.00   Max.   :407.0   Max.   :228.0   Max.   :236.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.80   1st Qu.: -6.0556   1st Qu.:  42.0  
 Median :  0.000   Median :42.11   Median : -2.9056   Median : 143.0  
 Mean   :  0.633   Mean   :41.33   Mean   : -2.9515   Mean   : 278.2  
 3rd Qu.:  0.000   3rd Qu.:43.12   3rd Qu.:  0.8192   3rd Qu.: 421.0  
 Max.   :599.000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

Visualización de 6 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=6)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=6)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin            precip           nevada 
 Min.   : 1.00   Min.   :-31.0   Min.   :-155.0   Min.   :  0.00   Min.   :0  
 1st Qu.:25.00   1st Qu.:173.0   1st Qu.:  76.0   1st Qu.:  0.00   1st Qu.:0  
 Median :34.00   Median :234.0   Median : 123.0   Median :  2.00   Median :0  
 Mean   :33.43   Mean   :229.6   Mean   : 118.4   Mean   : 11.92   Mean   :0  
 3rd Qu.:44.00   3rd Qu.:288.0   3rd Qu.: 165.0   3rd Qu.: 16.00   3rd Qu.:0  
 Max.   :53.00   Max.   :442.0   Max.   : 272.0   Max.   :131.00   Max.   :0  
   prof_nieve           longitud        latitud          altitud      
 Min.   :  0.00000   Min.   :35.28   Min.   :-8.624   Min.   :   1.0  
 1st Qu.:  0.00000   1st Qu.:39.47   1st Qu.:-5.598   1st Qu.:  44.0  
 Median :  0.00000   Median :41.22   Median :-2.138   Median : 192.0  
 Mean   :  0.03019   Mean   :40.62   Mean   :-2.207   Mean   : 339.4  
 3rd Qu.:  0.00000   3rd Qu.:42.22   3rd Qu.: 1.039   3rd Qu.: 609.0  
 Max.   :290.00000   Max.   :43.57   Max.   : 4.216   Max.   :1405.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin             precip      
 Min.   : 1.00   Min.   :-109.0   Min.   :-175.00   Min.   :  0.00  
 1st Qu.:14.00   1st Qu.:  30.0   1st Qu.: -32.00   1st Qu.:  3.00  
 Median :27.00   Median :  78.0   Median :  11.00   Median : 18.00  
 Mean   :26.81   Mean   :  84.4   Mean   :  14.45   Mean   : 31.78  
 3rd Qu.:40.00   3rd Qu.: 138.0   3rd Qu.:  63.00   3rd Qu.: 47.00  
 Max.   :53.00   Max.   : 299.0   Max.   : 183.00   Max.   :225.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.4992  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:42.18   1st Qu.:  0.7317  
 Median :0   Median :   0.000   Median :42.47   Median :  1.0544  
 Mean   :0   Mean   :   8.542   Mean   :42.23   Mean   :  0.7103  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.64   3rd Qu.:  1.7150  
 Max.   :0   Max.   :1073.000   Max.   :42.77   Max.   :  2.4378  
    altitud    
 Min.   :1097  
 1st Qu.:1971  
 Median :2230  
 Mean   :2159  
 3rd Qu.:2400  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip          nevada 
 Min.   : 1.00   Min.   :-13.0   Min.   :-44.0   Min.   : 55.0   Min.   :0  
 1st Qu.: 8.00   1st Qu.:126.0   1st Qu.: 56.0   1st Qu.: 85.0   1st Qu.:0  
 Median :15.00   Median :154.0   Median : 81.0   Median :112.0   Median :0  
 Mean   :20.26   Mean   :158.3   Mean   : 83.1   Mean   :118.2   Mean   :0  
 3rd Qu.:36.00   3rd Qu.:187.0   3rd Qu.:111.0   3rd Qu.:144.0   3rd Qu.:0  
 Max.   :53.00   Max.   :407.0   Max.   :228.0   Max.   :236.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.80   1st Qu.: -6.0556   1st Qu.:  42.0  
 Median :  0.000   Median :42.11   Median : -2.9056   Median : 143.0  
 Mean   :  0.633   Mean   :41.33   Mean   : -2.9515   Mean   : 278.2  
 3rd Qu.:  0.000   3rd Qu.:43.12   3rd Qu.:  0.8192   3rd Qu.: 421.0  
 Max.   :599.000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin             precip          nevada 
 Min.   : 1.00   Min.   :-22.0   Min.   :-189.00   Min.   : 0.00   Min.   :0  
 1st Qu.: 6.00   1st Qu.:128.0   1st Qu.:  21.00   1st Qu.: 0.00   1st Qu.:0  
 Median :11.00   Median :163.0   Median :  54.00   Median : 5.00   Median :0  
 Mean   :11.06   Mean   :165.2   Mean   :  56.37   Mean   :13.65   Mean   :0  
 3rd Qu.:16.00   3rd Qu.:200.0   3rd Qu.:  88.00   3rd Qu.:21.00   3rd Qu.:0  
 Max.   :31.00   Max.   :380.0   Max.   : 249.00   Max.   :93.00   Max.   :0  
   prof_nieve          longitud        latitud           altitud    
 Min.   :  0.0000   Min.   :36.85   Min.   :-6.6000   Min.   :   1  
 1st Qu.:  0.0000   1st Qu.:40.30   1st Qu.:-1.8853   1st Qu.:  71  
 Median :  0.0000   Median :41.29   Median : 0.5356   Median : 421  
 Mean   :  0.1225   Mean   :40.92   Mean   :-0.2914   Mean   : 444  
 3rd Qu.:  0.0000   3rd Qu.:41.91   3rd Qu.: 1.7678   3rd Qu.: 775  
 Max.   :299.0000   Max.   :43.49   Max.   : 4.2156   Max.   :1405  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

Visualización de 8 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=8)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=8)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin            precip           nevada 
 Min.   :18.00   Min.   :-31.0   Min.   :-155.0   Min.   : 0.000   Min.   :0  
 1st Qu.:28.00   1st Qu.:182.0   1st Qu.:  73.0   1st Qu.: 0.000   1st Qu.:0  
 Median :36.00   Median :254.0   Median : 126.0   Median : 1.000   Median :0  
 Mean   :36.13   Mean   :238.1   Mean   : 118.7   Mean   : 5.973   Mean   :0  
 3rd Qu.:45.00   3rd Qu.:297.0   3rd Qu.: 169.0   3rd Qu.: 9.000   3rd Qu.:0  
 Max.   :53.00   Max.   :428.0   Max.   : 272.0   Max.   :60.000   Max.   :0  
   prof_nieve           longitud        latitud           altitud    
 Min.   :  0.00000   Min.   :36.85   Min.   :-6.6000   Min.   :   1  
 1st Qu.:  0.00000   1st Qu.:40.47   1st Qu.:-2.4831   1st Qu.:  79  
 Median :  0.00000   Median :41.29   Median : 0.4914   Median : 427  
 Mean   :  0.02518   Mean   :41.03   Mean   :-0.4505   Mean   : 445  
 3rd Qu.:  0.00000   3rd Qu.:41.81   3rd Qu.: 1.6531   3rd Qu.: 704  
 Max.   :290.00000   Max.   :43.36   Max.   : 4.2156   Max.   :1405  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin             precip      
 Min.   : 1.00   Min.   :-109.0   Min.   :-175.00   Min.   :  0.00  
 1st Qu.:14.00   1st Qu.:  30.0   1st Qu.: -32.00   1st Qu.:  3.00  
 Median :27.00   Median :  78.0   Median :  11.00   Median : 18.00  
 Mean   :26.81   Mean   :  84.4   Mean   :  14.45   Mean   : 31.78  
 3rd Qu.:40.00   3rd Qu.: 138.0   3rd Qu.:  63.00   3rd Qu.: 47.00  
 Max.   :53.00   Max.   : 299.0   Max.   : 183.00   Max.   :225.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.4992  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:42.18   1st Qu.:  0.7317  
 Median :0   Median :   0.000   Median :42.47   Median :  1.0544  
 Mean   :0   Mean   :   8.542   Mean   :42.23   Mean   :  0.7103  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.64   3rd Qu.:  1.7150  
 Max.   :0   Max.   :1073.000   Max.   :42.77   Max.   :  2.4378  
    altitud    
 Min.   :1097  
 1st Qu.:1971  
 Median :2230  
 Mean   :2159  
 3rd Qu.:2400  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip           nevada 
 Min.   :24.00   Min.   : 14.0   Min.   :-61.0   Min.   : 30.00   Min.   :0  
 1st Qu.:38.00   1st Qu.:149.0   1st Qu.: 73.0   1st Qu.: 49.00   1st Qu.:0  
 Median :43.00   Median :191.0   Median :108.0   Median : 62.00   Median :0  
 Mean   :42.27   Mean   :194.1   Mean   :108.4   Mean   : 66.87   Mean   :0  
 3rd Qu.:47.00   3rd Qu.:238.0   3rd Qu.:145.0   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :53.00   Max.   :376.0   Max.   :247.0   Max.   :131.00   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :35.28   Min.   :-8.624   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:40.82   1st Qu.:-5.346   1st Qu.:  52.0  
 Median : 0.00000   Median :41.70   Median :-1.117   Median : 192.0  
 Mean   : 0.08761   Mean   :41.40   Mean   :-1.696   Mean   : 311.6  
 3rd Qu.: 0.00000   3rd Qu.:42.56   3rd Qu.: 1.768   3rd Qu.: 513.0  
 Max.   :52.00000   Max.   :43.57   Max.   : 4.216   Max.   :1405.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin           precip          nevada 
 Min.   : 1.00   Min.   :-13.0   Min.   :-44.0   Min.   : 55.0   Min.   :0  
 1st Qu.: 8.00   1st Qu.:126.0   1st Qu.: 56.0   1st Qu.: 85.0   1st Qu.:0  
 Median :15.00   Median :154.0   Median : 81.0   Median :112.0   Median :0  
 Mean   :20.26   Mean   :158.3   Mean   : 83.1   Mean   :118.2   Mean   :0  
 3rd Qu.:36.00   3rd Qu.:187.0   3rd Qu.:111.0   3rd Qu.:144.0   3rd Qu.:0  
 Max.   :53.00   Max.   :407.0   Max.   :228.0   Max.   :236.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.80   1st Qu.: -6.0556   1st Qu.:  42.0  
 Median :  0.000   Median :42.11   Median : -2.9056   Median : 143.0  
 Mean   :  0.633   Mean   :41.33   Mean   : -2.9515   Mean   : 278.2  
 3rd Qu.:  0.000   3rd Qu.:43.12   3rd Qu.:  0.8192   3rd Qu.: 421.0  
 Max.   :599.000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin             precip          nevada 
 Min.   : 1.00   Min.   :-22.0   Min.   :-189.00   Min.   : 0.00   Min.   :0  
 1st Qu.: 6.00   1st Qu.:128.0   1st Qu.:  21.00   1st Qu.: 0.00   1st Qu.:0  
 Median :11.00   Median :163.0   Median :  54.00   Median : 5.00   Median :0  
 Mean   :11.06   Mean   :165.2   Mean   :  56.37   Mean   :13.65   Mean   :0  
 3rd Qu.:16.00   3rd Qu.:200.0   3rd Qu.:  88.00   3rd Qu.:21.00   3rd Qu.:0  
 Max.   :31.00   Max.   :380.0   Max.   : 249.00   Max.   :93.00   Max.   :0  
   prof_nieve          longitud        latitud           altitud    
 Min.   :  0.0000   Min.   :36.85   Min.   :-6.6000   Min.   :   1  
 1st Qu.:  0.0000   1st Qu.:40.30   1st Qu.:-1.8853   1st Qu.:  71  
 Median :  0.0000   Median :41.29   Median : 0.5356   Median : 421  
 Mean   :  0.1225   Mean   :40.92   Mean   :-0.2914   Mean   : 444  
 3rd Qu.:  0.0000   3rd Qu.:41.91   3rd Qu.: 1.7678   3rd Qu.: 775  
 Max.   :299.0000   Max.   :43.49   Max.   : 4.2156   Max.   :1405  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin           precip          nevada 
 Min.   : 1.00   Min.   : 46.0   Min.   :-68.0   Min.   : 0.00   Min.   :0  
 1st Qu.:14.00   1st Qu.:172.0   1st Qu.: 81.0   1st Qu.: 0.00   1st Qu.:0  
 Median :26.00   Median :214.0   Median :122.0   Median : 2.00   Median :0  
 Mean   :25.99   Mean   :221.4   Mean   :120.4   Mean   :10.41   Mean   :0  
 3rd Qu.:38.00   3rd Qu.:264.0   3rd Qu.:161.0   3rd Qu.:17.00   3rd Qu.:0  
 Max.   :53.00   Max.   :442.0   Max.   :269.0   Max.   :68.00   Max.   :0  
   prof_nieve           longitud        latitud          altitud     
 Min.   :  0.00000   Min.   :35.28   Min.   :-8.624   Min.   :  1.0  
 1st Qu.:  0.00000   1st Qu.:36.75   1st Qu.:-6.909   1st Qu.: 22.0  
 Median :  0.00000   Median :37.84   Median :-5.879   Median : 87.0  
 Mean   :  0.02622   Mean   :39.63   Mean   :-5.797   Mean   :137.8  
 3rd Qu.:  0.00000   3rd Qu.:43.30   3rd Qu.:-4.488   3rd Qu.:185.0  
 Max.   :177.00000   Max.   :43.57   Max.   :-1.169   Max.   :656.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

Visualización de 10 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=10)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=10)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)
  df.cluster09 <- subset(df, cluster==9)
  df.cluster10 <- subset(df, cluster==10)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin            precip           nevada 
 Min.   :18.00   Min.   :-31.0   Min.   :-155.0   Min.   : 0.000   Min.   :0  
 1st Qu.:28.00   1st Qu.:182.0   1st Qu.:  73.0   1st Qu.: 0.000   1st Qu.:0  
 Median :36.00   Median :254.0   Median : 126.0   Median : 1.000   Median :0  
 Mean   :36.13   Mean   :238.1   Mean   : 118.7   Mean   : 5.973   Mean   :0  
 3rd Qu.:45.00   3rd Qu.:297.0   3rd Qu.: 169.0   3rd Qu.: 9.000   3rd Qu.:0  
 Max.   :53.00   Max.   :428.0   Max.   : 272.0   Max.   :60.000   Max.   :0  
   prof_nieve           longitud        latitud           altitud    
 Min.   :  0.00000   Min.   :36.85   Min.   :-6.6000   Min.   :   1  
 1st Qu.:  0.00000   1st Qu.:40.47   1st Qu.:-2.4831   1st Qu.:  79  
 Median :  0.00000   Median :41.29   Median : 0.4914   Median : 427  
 Mean   :  0.02518   Mean   :41.03   Mean   :-0.4505   Mean   : 445  
 3rd Qu.:  0.00000   3rd Qu.:41.81   3rd Qu.: 1.6531   3rd Qu.: 704  
 Max.   :290.00000   Max.   :43.36   Max.   : 4.2156   Max.   :1405  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax              tmin             precip     
 Min.   : 1.00   Min.   :-109.00   Min.   :-175.00   Min.   : 0.00  
 1st Qu.:13.00   1st Qu.:  33.00   1st Qu.: -30.00   1st Qu.: 1.00  
 Median :28.00   Median :  82.00   Median :  14.00   Median :12.00  
 Mean   :26.84   Mean   :  87.99   Mean   :  16.76   Mean   :17.44  
 3rd Qu.:40.00   3rd Qu.: 143.00   3rd Qu.:  67.75   3rd Qu.:29.00  
 Max.   :53.00   Max.   : 299.00   Max.   : 183.00   Max.   :76.00  
     nevada    prof_nieve         longitud        latitud           altitud    
 Min.   :0   Min.   :  0.000   Min.   :40.78   Min.   :-4.0103   Min.   :1572  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:42.18   1st Qu.: 0.7317   1st Qu.:1971  
 Median :0   Median :  0.000   Median :42.47   Median : 1.0544   Median :2230  
 Mean   :0   Mean   :  6.992   Mean   :42.23   Mean   : 0.7202   Mean   :2161  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:42.64   3rd Qu.: 1.7150   3rd Qu.:2400  
 Max.   :0   Max.   :939.000   Max.   :42.77   Max.   : 2.4378   Max.   :2535  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin           precip           nevada 
 Min.   :24.00   Min.   : 14.0   Min.   :-61.0   Min.   : 30.00   Min.   :0  
 1st Qu.:38.00   1st Qu.:149.0   1st Qu.: 73.0   1st Qu.: 49.00   1st Qu.:0  
 Median :43.00   Median :191.0   Median :108.0   Median : 62.00   Median :0  
 Mean   :42.27   Mean   :194.1   Mean   :108.4   Mean   : 66.87   Mean   :0  
 3rd Qu.:47.00   3rd Qu.:238.0   3rd Qu.:145.0   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :53.00   Max.   :376.0   Max.   :247.0   Max.   :131.00   Max.   :0  
   prof_nieve          longitud        latitud          altitud      
 Min.   : 0.00000   Min.   :35.28   Min.   :-8.624   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:40.82   1st Qu.:-5.346   1st Qu.:  52.0  
 Median : 0.00000   Median :41.70   Median :-1.117   Median : 192.0  
 Mean   : 0.08761   Mean   :41.40   Mean   :-1.696   Mean   : 311.6  
 3rd Qu.: 0.00000   3rd Qu.:42.56   3rd Qu.: 1.768   3rd Qu.: 513.0  
 Max.   :52.00000   Max.   :43.57   Max.   : 4.216   Max.   :1405.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin           precip          nevada 
 Min.   : 1.00   Min.   :-13.0   Min.   :-44.0   Min.   : 55.0   Min.   :0  
 1st Qu.: 8.00   1st Qu.:126.0   1st Qu.: 56.0   1st Qu.: 85.0   1st Qu.:0  
 Median :15.00   Median :154.0   Median : 81.0   Median :112.0   Median :0  
 Mean   :20.26   Mean   :158.3   Mean   : 83.1   Mean   :118.2   Mean   :0  
 3rd Qu.:36.00   3rd Qu.:187.0   3rd Qu.:111.0   3rd Qu.:144.0   3rd Qu.:0  
 Max.   :53.00   Max.   :407.0   Max.   :228.0   Max.   :236.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.80   1st Qu.: -6.0556   1st Qu.:  42.0  
 Median :  0.000   Median :42.11   Median : -2.9056   Median : 143.0  
 Mean   :  0.633   Mean   :41.33   Mean   : -2.9515   Mean   : 278.2  
 3rd Qu.:  0.000   3rd Qu.:43.12   3rd Qu.:  0.8192   3rd Qu.: 421.0  
 Max.   :599.000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin             precip          nevada 
 Min.   : 1.00   Min.   :-22.0   Min.   :-189.00   Min.   : 0.00   Min.   :0  
 1st Qu.: 6.00   1st Qu.:128.0   1st Qu.:  21.00   1st Qu.: 0.00   1st Qu.:0  
 Median :11.00   Median :163.0   Median :  54.00   Median : 5.00   Median :0  
 Mean   :11.06   Mean   :165.2   Mean   :  56.37   Mean   :13.65   Mean   :0  
 3rd Qu.:16.00   3rd Qu.:200.0   3rd Qu.:  88.00   3rd Qu.:21.00   3rd Qu.:0  
 Max.   :31.00   Max.   :380.0   Max.   : 249.00   Max.   :93.00   Max.   :0  
   prof_nieve          longitud        latitud           altitud    
 Min.   :  0.0000   Min.   :36.85   Min.   :-6.6000   Min.   :   1  
 1st Qu.:  0.0000   1st Qu.:40.30   1st Qu.:-1.8853   1st Qu.:  71  
 Median :  0.0000   Median :41.29   Median : 0.5356   Median : 421  
 Mean   :  0.1225   Mean   :40.92   Mean   :-0.2914   Mean   : 444  
 3rd Qu.:  0.0000   3rd Qu.:41.91   3rd Qu.: 1.7678   3rd Qu.: 775  
 Max.   :299.0000   Max.   :43.49   Max.   : 4.2156   Max.   :1405  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin           precip         nevada 
 Min.   : 1.00   Min.   : 46.0   Min.   :-68.0   Min.   : 0.0   Min.   :0  
 1st Qu.:14.00   1st Qu.:155.0   1st Qu.: 72.0   1st Qu.: 1.0   1st Qu.:0  
 Median :25.00   Median :198.0   Median :107.0   Median :10.0   Median :0  
 Mean   :25.23   Mean   :196.5   Mean   :104.6   Mean   :14.8   Mean   :0  
 3rd Qu.:36.00   3rd Qu.:233.0   3rd Qu.:142.0   3rd Qu.:24.0   3rd Qu.:0  
 Max.   :53.00   Max.   :398.0   Max.   :216.0   Max.   :65.0   Max.   :0  
   prof_nieve           longitud        latitud          altitud   
 Min.   :  0.00000   Min.   :39.47   Min.   :-8.624   Min.   :  4  
 1st Qu.:  0.00000   1st Qu.:42.56   1st Qu.:-8.411   1st Qu.: 52  
 Median :  0.00000   Median :43.31   Median :-6.600   Median :127  
 Mean   :  0.05589   Mean   :43.08   Mean   :-6.517   Mean   :181  
 3rd Qu.:  0.00000   3rd Qu.:43.43   3rd Qu.:-5.698   3rd Qu.:336  
 Max.   :177.00000   Max.   :43.57   Max.   :-1.636   Max.   :656  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt          tmax            tmin           precip           nevada 
 Min.   : 1.00   Min.   : 73.0   Min.   :-65.0   Min.   : 0.000   Min.   :0  
 1st Qu.:14.00   1st Qu.:183.0   1st Qu.: 91.0   1st Qu.: 0.000   1st Qu.:0  
 Median :27.00   Median :233.0   Median :134.0   Median : 0.000   Median :0  
 Mean   :26.56   Mean   :240.5   Mean   :132.3   Mean   : 7.059   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:293.0   3rd Qu.:180.0   3rd Qu.: 8.000   3rd Qu.:0  
 Max.   :53.00   Max.   :442.0   Max.   :269.0   Max.   :68.000   Max.   :0  
   prof_nieve           longitud        latitud          altitud   
 Min.   : 0.000000   Min.   :35.28   Min.   :-6.949   Min.   :  1  
 1st Qu.: 0.000000   1st Qu.:36.64   1st Qu.:-6.257   1st Qu.: 21  
 Median : 0.000000   Median :36.85   Median :-5.600   Median : 34  
 Mean   : 0.003605   Mean   :36.99   Mean   :-5.248   Mean   :105  
 3rd Qu.: 0.000000   3rd Qu.:37.28   3rd Qu.:-4.488   3rd Qu.: 90  
 Max.   :13.000000   Max.   :39.47   Max.   :-1.169   Max.   :582  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt          tmax            tmin           precip            nevada 
 Min.   : 1.00   Min.   :  1.0   Min.   :-51.0   Min.   :  0.000   Min.   :0  
 1st Qu.:13.00   1st Qu.:211.0   1st Qu.:149.0   1st Qu.:  0.000   1st Qu.:0  
 Median :26.00   Median :234.0   Median :172.0   Median :  0.000   Median :0  
 Mean   :26.29   Mean   :229.7   Mean   :165.3   Mean   :  5.298   Mean   :0  
 3rd Qu.:39.00   3rd Qu.:262.0   3rd Qu.:201.0   3rd Qu.:  2.000   3rd Qu.:0  
 Max.   :53.00   Max.   :384.0   Max.   :263.0   Max.   :155.000   Max.   :0  
   prof_nieve    longitud        latitud          altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.89   Min.   :  14.0  
 1st Qu.:0    1st Qu.:28.05   1st Qu.:-16.56   1st Qu.:  25.0  
 Median :0    Median :28.44   Median :-16.33   Median :  33.0  
 Mean   :0    Mean   :28.35   Mean   :-16.03   Mean   : 368.5  
 3rd Qu.:0    3rd Qu.:28.48   3rd Qu.:-15.39   3rd Qu.:  64.0  
 Max.   :0    Max.   :28.95   Max.   :-13.60   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster09)

   fecha_cnt          tmax             tmin              precip      
 Min.   : 1.00   Min.   :-69.00   Min.   :-131.000   Min.   : 54.00  
 1st Qu.:16.00   1st Qu.: 18.00   1st Qu.: -40.000   1st Qu.: 73.00  
 Median :24.00   Median : 61.00   Median :   2.000   Median : 90.00  
 Mean   :26.66   Mean   : 67.67   Mean   :   3.735   Mean   : 98.54  
 3rd Qu.:40.00   3rd Qu.:111.00   3rd Qu.:  45.000   3rd Qu.:117.00  
 Max.   :53.00   Max.   :262.00   Max.   : 153.000   Max.   :225.00  
     nevada    prof_nieve         longitud        latitud        
 Min.   :0   Min.   :   0.00   Min.   :28.31   Min.   :-16.4992  
 1st Qu.:0   1st Qu.:   0.00   1st Qu.:42.18   1st Qu.:  0.7789  
 Median :0   Median :   0.00   Median :42.47   Median :  1.0544  
 Mean   :0   Mean   :  15.76   Mean   :42.21   Mean   :  0.6641  
 3rd Qu.:0   3rd Qu.:   0.00   3rd Qu.:42.64   3rd Qu.:  1.7150  
 Max.   :0   Max.   :1073.00   Max.   :42.77   Max.   :  2.4378  
    altitud    
 Min.   :1097  
 1st Qu.:1894  
 Median :2247  
 Mean   :2154  
 3rd Qu.:2400  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster10)

   fecha_cnt          tmax            tmin             precip        nevada 
 Min.   : 1.00   Min.   :-58.0   Min.   :-103.00   Min.   :222   Min.   :0  
 1st Qu.:10.00   1st Qu.:100.0   1st Qu.:  41.00   1st Qu.:246   1st Qu.:0  
 Median :40.00   Median :136.0   Median :  77.00   Median :278   Median :0  
 Mean   :29.64   Mean   :133.8   Mean   :  72.47   Mean   :299   Mean   :0  
 3rd Qu.:46.00   3rd Qu.:181.0   3rd Qu.: 118.00   3rd Qu.:327   3rd Qu.:0  
 Max.   :53.00   Max.   :331.0   Max.   : 217.00   Max.   :690   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.78   1st Qu.: -5.600   1st Qu.:  61.0  
 Median :  0.000   Median :41.98   Median : -0.482   Median : 261.0  
 Mean   :  5.451   Mean   :40.77   Mean   : -2.548   Mean   : 707.2  
 3rd Qu.:  0.000   3rd Qu.:42.53   3rd Qu.:  1.099   3rd Qu.:1055.0  
 Max.   :685.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.hist(df.cluster09)

if (!empty_nodes) mpr.hist(df.cluster10)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster09)

if (!empty_nodes) mpr.boxplot(df.cluster10)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
  df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
  df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)